Geocoding to Manage Missing Data in a Secondary Analysis of Community-Dwelling, Low-Income Older Adults.
Kathy Wright, Shirley M Moore, Diana Lynn Morris and 1 others
PMID 28742924WHAT IT FOUND
Geocoding linked census data to older adults' addresses, filling 100% of missing income and neighborhood poverty information in a secondary analysis.
This method allowed researchers to characterize low-income Medicare-Medicaid enrollees without new data collection.
Key findings
01Geocoding successfully filled all previously missing income and neighborhood poverty data in the secondary analysis.
02Participants were predominantly female, White, with less than a high school education, working in service industries before retirement.
03Average neighborhood poverty was 22.33%, higher than the 2010 US population average of 15.1%.
STILL TO COME
How it was doneWhat they foundWhat it means for RNs
Read the rest of this summary
You get three full summaries a month, free, and we do not ask for a card. Search, the TL;DRs and your library stay unlimited either way.
What it does not show
The study used pre-retirement occupation as a proxy for income rather than collecting actual current income data. Years of retirement were not accounted for, meaning income data reflects the past rather than the participant's financial status at the time of the study. Only 2010 ACS data were used, which may not reflect current neighborhood conditions for participants who retired more than 10 years prior. This is a methodological exemplar, not a clinical trial, so it does not test the efficacy of any intervention.
Declared interests
The authors disclosed no potential conflicts of interest, financial or otherwise.
The easy way to misread this
Do not interpret the geocoded neighborhood poverty rates or occupation-based income estimates as the participants' actual current personal income or assets. These are aggregated geographic proxies, not individual financial records, and may not reflect the participants' true economic status at the time of the study.